Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine
Kasun, Liyanaarachchi Lekamalage Chamara
Date of Issue2015
School of Electrical and Electronic Engineering
Generic object recognition is to classify the object to a generic category. Intra-class variabilities cause big troubles for this task. Traditional methods involve plenty of pre-processing steps, like model construction, feature extraction, etc. Moreover, these methods are only effective for some specific dataset. In this paper, we propose to use local receptive fields based extreme learning machine (ELM-LRF) as a general framework for object recognition. It is operated directly on the raw images and thus suitable for all different datasets. Additionally, the architecture is simple and only requires few computations, as most connection weights are randomly generated. Comparing to state-of-the-art results on NORB, ETH-80 and COIL datasets, it is on par with the best one on ETH-80 and sets the new records for NORB and COIL.
Generic object recognition; local receptive fields; Extreme Learning Machine (ELM)
Procedia Computer Science
© 2015 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.